Our faculty are engaged in many different areas of research, many working closely with faculty from other departments. Below you will find links the laboratory pages and project websites of some of our faculty. But, the best way to find out what we are up to is to browse our faculty listing and look at the research interests and publications of each professor.
- Artificial Intelligence Institute. The institute emphasizes AI applications and impact through extensive interdisciplinary collaborations with 10 colleges and several major centers and active research groups across the university. Each partner group has identified cases involving a significant big data challenge that needs to be solved with AI techniques
- AISys Lab (Artificial Intelligence and Systems Laboratory). AISys Lab investigate a variety of open problems that sit at the intersection of artificial intelligence (AI), machine learning (ML), and computer systems (e.g., embedded, cloud, robotics). We investigate the development of novel algorithmic and theoretically principled ML methods for systems problems such as optimizing the performance and energy efficiency of highly-configurable systems. We also look into the design and architecture of system software that treat ML computation as a first-class citizen such as optimizing training and inference. Our overarching goal is to develop the next generation of on-device and cloud-based systems able to perceive, reason and react to complex real-world environments and users with high levels of precision and efficiency. We aim to conduct cutting-edge and high impact research through full-stack approaches that encourage lab members with skills in algorithms, systems, statistics, mathematics and software to work closely together to solve critical and practical challenges in the areas at the intersection of AI+Systems.
- Computer Vision Laboratory. At the Computer Vision Lab of the University of South Carolina (USC), we have been conducting exciting research related to computer vision, image processing and machine learning. Our main goals are to simulate basic human vision functions with computers and to develop novel image processing and machine learning techniques to deal with practical applications. Current and past sponsors of our research include the National Science Foundation (NSF), Air Force Office of Scientific Research (AFOSR), National Endowment for the Humanities (NEH), Defense Advanced Research Projects Agency (DARPA), and the University of South Carolina.
- Machine Learning and Evolution Laboratory. Our research focuses on development of deep learning, machine learning, big data, and evolutionary algorithms for knowledge discovery and innovation in materials informatics, bioinformatics, health informatics, and other challenging problems of science, and engineering. We have worked on deep learning based materials discovery, cellular image segmentation, audio based fault diagnosis and audio event detection, intelligent manufacturing, text mining, DNA regulatory motif discovery, microarray analysis, disease gene discovery, protein-DNA/RNA/peptide binding, and protein function prediction.
- Center for Computational Robotics. Facilitates research, education, and outreach in robotics. Our mission is to solve complex scientific problems in perception, autonomy, and interaction for robots that operate in unstructured environments. CCR projects span theoretical foundational and fielded applications. The center was established in 1983 as the Center for Machine Intelligence and assumed its current name, reflecting a renewed focus on robotics, in 2015
- Autonomous Field Robotics Laboratory. The goal of the AFRL is to research mobile robotics and in particular cooperating intelligent agents with application to multi-robot cooperative localization, mapping, exploration and coverage. Most robotic platforms, after successful development and testing in a laboratory setting, are deployed in the real world. It is at that moment where reality interferes with theory and several assumptions are proven wrong. AFRL studies the interaction of robots with the real world, modeling uncertainty, and deriving efficient strategies to move, sense, and interact with the environment. Coastal marine environments and underwater caves are the primary focus of our research utilized a wide range of vehicles and sensors.
Bioinformatics, Computational Biology and Medicine
- Bioinformatics and Computational Biology Group. Our faculty are engaged in leading-edge research and are dedicated to educating the next generation of researchers and practitioners in bioinformatics and computational biology.
- Computational Biology Research Lab. The general focus of Dr. Valafar’s current research is the application and transfer of engineering techniques to biological systems. In addition, this transfer of information can be reversed to implement efficient optimization and security (immunity) techniques inspired by biological models. His specific research is divided into two main categories: Computational Biology and Computational Medicine.
- Geneorder.org. MLGO is a web server designed for genome rearrangement and gene order analysis, developed by Dr. Jijun Tang and students. It can be used to infer a phylogeny from genome rearrangement and gene order data, and can also obtain an estimation of ancestral genomes, given an input tree. It can handle large scale data with the size ranges from mitochandrial to nuclear genomes. Besides rearrangements, it can also handle gene insertion, deletion and duplication.
- Center for Information Assurance Engineering. A research and educational unit at the USC. It is a part of the ongoing effort at USC to increase information systems security awareness and develop high quality education and research in this area. Faculty and students associated with the CIAE are working in a broad spectrum of security topics and issues. Our goal is to become one of the leading academic institutes in the Unites States in Information Security Education.
- SPID: Secure Protocol Implementation & Development Group. Our lab is dedicated to implementation and development of secure protocols for computer networks and distributed systems. We SPIDers here are currently conducting research in the areas of network anomaly detection, secure sensor network infrastructures, and security of intermediate network devices. Our focus is not only on the implementation and development of secure protocols, but also on the verification of their correctness, because incorrect implementation of security can be even worse than no security. To ensure the practicability of our protocols, we also consider and evaluate the tradeoff between security and efficiency in the development process. We look forward to making contributions in the collective efforts to fight various network security threats and make the Internet a more secure place.
- Heterogeneous and Reconfigurable Computing (HeRC) Group. At the Heterogeneous and Reconfigurable Computing Research Center, we focus on analysis and implementation of computationally intensive applications on coprocessors such as FPGAs and GPUs, to accelerate the applications.
- At Intelligent Circuits, Architectures, and Systems (iCAS) Lab, we focus on alternatives to von-Neumann computing architectures for emerging applications such as neuromorphic computing, quantum computing, edge computing, and Internet-of-Things (IoT). Viable solutions to the challenge of designing these emerging computing systems span the interrelated fields of machine learning, computer architecture, circuit design, and the potential to leverage beyond-CMOS nanoscale technologies such as spintronic devices.
- Ward One. Using the affordances of both touchscreen and desktop interfaces, Ward One invites participants to understand the vibrant but insular world that African Americans created in the aftermath of Emancipation and the community that formed in the face of Jim Crow policies and segregation—in the shadows of the Statehouse and the University of South Carolina.
- Wikitheoria.com. A new way to share and collaborate on researchable ideas in Sociology. It uses a form of web-based crowdsourcing. This means lots of people contributing only a small effort to enhance whatever part of the system interests them the most.